首页> 外文期刊>Journal of hydrologic engineering >Discussion of 'Monthly Mean Streamflow Prediction Based on Bat Algorithm-Support Vector Machine' by Bing Xing, Rong Gan, Guodong Liu, Zhongfang Liu, Jing Zhang, and Yufeng Ren
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Discussion of 'Monthly Mean Streamflow Prediction Based on Bat Algorithm-Support Vector Machine' by Bing Xing, Rong Gan, Guodong Liu, Zhongfang Liu, Jing Zhang, and Yufeng Ren

机译:邢兵,甘荣,刘国栋,刘中芳,张静,任玉峰对“基于蝙蝠算法-支持向量机的月平均流量预测”的讨论

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摘要

The discusser wishes to thank the authors for exploring the exactness of a bat algorithm-based support vector machine (BA-SVM) in forecasting monthly streamflows of the Yangtze River in China by using previous rainfall and streamflow data. The BA-SVM results were compared with artificial neural networks (ANNs) and cross validation-based SVM (CV-SVM). The results indicated that the BA-SVM model provided better accuracy than the ANN and CV-SVM models in monthly streamflow forecasting. The discusser needs to bring up the some critical perspectives, which the authors and other potential researchers may consider: 1. Xing et al. (2015) measured the monthly mean streamflow and precipitation data for the period of 1952-2011 in Yichang Station, China. They utilized data from 1952 to 1999 for training and the remaining data for testing. A basic issue in training an ANN is abstaining from overfitting as it decreases its generalization exactness. If an excess of neurons is utilized, the network has an excess of parameters and may overfit the data. Conversely, if a couple of neurons are excessively incorporated into the network, it would not be conceivable to completely recognize the signal and fluctuation of a complex data set (Cimen and Kisi 2009). In the study, the authors used 48 years of monthly data (48 × 12 = 576 monthly values). For the optimal ANN (7,14,1) model, 112 (7 × 14+14=112) weights were utilized. The training data do not appear to be sufficient to abstain from over-fitting. The authors could use fewer hidden node numbers for the ANN models and obtain better results this way.
机译:讨论者希望感谢作者探索了基于蝙蝠算法的支持向量机(BA-SVM)在使用先前的降雨和流量数据预测中国长江的月流量的准确性。将BA-SVM结果与人工神经网络(ANN)和基于交叉验证的SVM(CV-SVM)进行了比较。结果表明,在每月流量预测中,BA-SVM模型的准确性优于ANN和CV-SVM模型。讨论者需要提出一些重要的观点,作者和其他潜在的研究者可能会考虑:1.邢等。 (2015年)测量了中国宜昌站1952-2011年的月平均流量和降水数据。他们利用1952年至1999年的数据进行培训,其余数据用于测试。训练ANN的一个基本问题是避免过度拟合,因为它会降低泛化的准确性。如果使用了过多的神经元,则网络将具有过多的参数,并且可能会过度拟合数据。相反,如果将两个神经元过多地整合到网络中,则无法完全识别复杂数据集的信号和波动(Cimen和Kisi 2009)。在这项研究中,作者使用了48年的月度数据(48×12 = 576的月度值)。对于最佳的ANN(7,14,1)模型,使用了112(7×14 + 14 = 112)个权重。训练数据似乎不足以避免过度拟合。作者可以为神经网络模型使用更少的隐藏节点数,并以此方式获得更好的结果。

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  • 来源
    《Journal of hydrologic engineering》 |2016年第8期|07016010.1-07016010.1|共1页
  • 作者

    Ozgur Kisi;

  • 作者单位

    Dept. of Civil Engineering, Canik Basari Univ., Samsun 55080, Turkey;

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  • 正文语种 eng
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  • 入库时间 2022-08-18 00:48:39

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